While sequence learning tasks have been studied in the literature [1]}, [2]}, [3]}, the work most related to MPN is [2]}, which focuses on training spiking neural networks with biologically plausible rules. While both [2]} and this work attempt to learn the distribution of sequences, they differ in the model, the learning rule, and the applications.
Regarding model assumptions, [2]} assumes a firing probability to be sigmoidal during \(T\)  discrete-time bins; MPN, however, assumes a continuous-time neuron model with exponentially distributed refractory periods
and stochastic neurons. Such different models and assumptions lead to distinct learning rules.
Finally, while [2]} exploits hidden units to learn a sequence, the MPN reported here consist of only visible neurons.
We expect that, by adding hidden neurons, MPN will become even more expressive. We leave hidden neurons problem to be explored in future work.
